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Deep Reinforcement Learning in R (Deep Q Learning, Policy Gradient, Actor-Critic Method, etc)

Modular PyTorch implementation of policy gradient methods

Deep Reinforcement Learning Policy Gradients Method - Pong game - Keras

OpenAI's Gym Car-Racing-V0 environment was tackled and, subsequently, solved using a variety of Reinforcement Learning methods including Deep Q-Network (DQN), Double Deep Q-Network (DDQN) and Deep Deterministic Policy Gradient (DDPG). This repository contains the code, as well as results from the... Read more

Implementation of the Fast Efficient Hyperparameter Tuning for Policy Gradient Methods https://arxiv.org/abs/1902.06583

These are experiments for examining reproducibility in Policy Gradient RL algorithms in Continuous domains. Mainly using the Rllab implementation.

Reimplementation of simple policy gradient algorithms such as REINFORCE and Actor-Critic methods.

An Empirical Analysis of Gradient Descent Optimization in Policy Gradient Methods - EWRL Workshop 2018

Implementation of the MEPOL algorithm - A policy gradient method for task-agnostic exploration

Policy gradient methods are a type of reinforcement learning techniques that rely upon optimizing parametrized policies with respect to the expected return (long-term cumulative reward) by gradient descent. In the off-policy algorithm, actions are sample using behaviour policy and separate target... Read more